To properly care for human subjects, as well as animals, it is necessary to ascertain information pertaining to concentrations of certain blood constituents, and other body fluids. For example, diabetics must periodically monitor their blood glucose, sometimes as often as several times daily. This information is necessary, so that insulin adjustments can be made to facilitate control of diabetes mellitus. According to a position statement of the American Diabetes Association in an article published in Clinical Diabetes, Volume 11, Number 4, pages 91-96, entitled: "Implications of the Diabetes Control and Complications Trial," tight control, or intensive therapy, could reduce many of the problems associated with diabetes mellitus. If proper monitoring of blood glucose and resultant insulin adjustments are not carried out, many physiological problems can occur for the diabetic patient.
Among problems that can occur are diabetic ketoacidosis, hyperosmolar hyperglycemia non-ketotic coma, and hypoglycemia. More devastating chronic developments include microvascular, neuropathic, and macrovascular disorders leading to blindness, renal failure, limb amputation, heart disease and stroke. Some of these problems can occur even with strict adherence to diet, exercise, blood glucose monitoring with current monitoring systems, and insulin replacement.
Major reasons that can account for these problems include the fact that blood glucose must be monitored as often as necessary to provide tight control of large insulin-glucose fluctuations (hypoglycemia-hyperglycemia), as described, for example, in an article entitled "Diabetes Patient Education Programs," by M. Wheeler et al, Diabetes. Care, Vol. 15, Supl.1, pp 36-40, 1992. An unfortunate impediment to the lack of patient adherence to proper monitoring is that fact that current methods to monitor blood glucose require a finger prick, which can be painful and can precipitate the contraction of infectious diseases. Due to lack of patient adherence (causes may include fear of finger prick, forgetfulness, apathy) blood glucose monitoring may not be conducted at the proper frequency, as described in an article by R. Surwit et al., entitled "The Role of Behavior in Diabetes Care," Diabetes Care. Vol. 5, No. 3, pgs. 337-342, 1982.
In addition, Type I diabetics can experience abrupt fluctuations in glucose concentrations during the intervals between scheduled tests despite their strict adherence to proper diet, exercise, and insulin replacement. Further, in some cases with certain types of home blood glucose monitoring systems, inaccurate readings can occur. (See, for example, an article by V. Laus et al., entitled: "Potential Pitfalls in the Use of Glucoscan and Glucoscan II Meters for Self-monitoring of Blood Glucose", Diabetes Care. Vol. 7, pgs. 590-594, 1984.)
Such fluctuations between test intervals underscore the need for the development of a non-invasive glucose monitoring sensor that can be used as often as medically needed, even continuously. (Some Type II diabetics must monitor their blood glucose levels on a daily basis, and timely and precise monitoring of blood glucose will improve the health of diabetics and improve their quality of life by reducing the long-term effects of the disease.)
A variety of systems have been proposed to monitor blood glucose non-invasively. For example, the Kaiser U.S. Pat. No. 4,169,676, describes a system for determining of blood glucose concentration by irradiating biological fluids with a CO.sub.2 (carbon dioxide) laser. The laser beam is coupled to the sample by way of an attenuated total reflectance (ATR) prism. Kaiser describes that using a laser source in infrared (IR) spectroscopy provides an improvement of about one hundred times in measurement sensitivity over conventional techniques, and a monochromatic laser source considerably improves the resolution. However, even with improved resolution and sensitivity, this method basically relies on using single wavelength `absorption` intensity data to determine varying concentrations of blood glucose. No consideration is given to interactions with other substances, and how such interactions may interfere with concentration measurements. As a consequence, this approach, using a single wavelength (or univariate analysis), yields highly unreliable results. In addition, a CO.sub.2 laser source is impractical because of its bulky size and because it generates unsafe amounts of heat for clinical use.
The Muller U.S. Pat. No. 4,427,889, describes a mechanism for determining blood glucose concentrations that utilizes a single beam laser operating at two wavelengths in the mid-infrared (IR) region to irradiate a multi-component sample, selected from whole blood or urine. The first measured wavelength lies within the infrared spectral range of 10.53 to 10.64 microns, and the second wavelength lies between 9.13 to 9.17 microns. The measurement is standardized by forming the ratio of `absorption` values of the first and second wavelengths. Glucose concentration is proportional to the absorption value that is measured at the second wavelength, while there is no glucose absorption at the first wavelength which provides a baseline absorption for the sample. Unfortunately, this approach has the same basic problem as the Kaiser scheme, described above.
The Dahne et al U.S. Pat. No. 4,655,225, uses near-IR spectroscopy for non-invasive determination of blood glucose (or glucose in tissues). A near-IR source in the 1000 to 2500 nm range is used to transmit light `through` a finger or earlobe. The patentees also describe a method for analyzing near-IR energy that is diffusely reflected from `deep within` the irradiated tissue. Spectroscopic responses are taken at two different wavelengths to quantify glucose. One wavelength is used to establish background absorption, while the other is used for determining glucose absorption. Concentrations of glucose are determined from the ratio of the two wavelengths. As in the case of Muller and Kaiser, this approach is not reliable because it relies on univariate analysis.
The Robinson et al U.S. Pat. No. 4,975,581, describes a technique to quantify glucose concentrations through the use of both a mid-IR light source using an ATR crystal and a broad-spectrum near-IR light source (having a wavelength on the order of 500 to 1000 nm). The patentees acknowledge the need for multivariate analysis to improve analysis precision over univariate analysis. This is accomplished by comparing the similarity of multiple wavelengths of IR energy obtained from an irradiated sample to that of a calibration model, obtained by the methods of partial least squares and principal component regression (chemometric analysis). The calibration model employed by Robinson et al. is a function of the concentration of materials in known samples as a function of absorption at several wavelengths of infrared energy.
Although the Robinson et al. '581 patent also acknowledges the importance of identifying and removing outlier samples from the calibration set, an outlier category of importance is not necessarily one of anomalies associated with instrumentation, positioning of the finger in the instrument, etc., but may result from insufficient calibration of the comparative model due to, for example, molecular interactions that have not previously been considered during model calibration. In the latter case, outlier data is essential for further "tuning" of the model to increase accuracy and precision. Removal of the outlier data, as opposed to utilizing it for model "tuning" would actually degrade the instrument's accuracy and precision. Therefore, concentration decisions based on a chemometric calibration model may not yield robust results. Also, the use of photodiode array elements in the Robinson et al patent does not take advantage of the capabilities of other detector array systems to remove noise. The responsivity of photodiode elements is less than those used in other detector array systems.
They also overlook fundamental principles of the optical properties of skin. For example, studies reported in an article by Hardy et al, entitled "Spectral Transmittance and Reflectance of Excised Human Skin", Journal of Applied Physics, Vol. 9, pp 257-264, 1956, which describes measurements of transmission and remission of an incident beam through skin samples of various thicknesses, including both the epidermis and various amounts of dermis, reveal that, as the thickness of the dermis increases, transmission decreases, and becomes more diffuse, suggesting multiple scattering, as described by R. Anderson et al, in "Optical Properties of Human Skin", The Science of Photomedicine, Plenum Press, N.Y., pgs. 147-194, 1982. From studies such as these, it was determined that the Lambert-Beer law is invalid for visible and NIR (near-infrared) wavelengths, when skin thickness exceeds 0.5 mm, which leads to nonlinear results that must be accounted for and corrected.
In an article entitled "Non-invasive Glucose Monitoring in Diabetic Patients: A Preliminary Evaluation", by Robinson et al., Clinical Chemistry, Vol. 38 No. 9, 1992, pp 1618-1622, the authors state that the "relative predictive abilities of these methods were examined in detail for the situation when Beer's law is followed. However, in complex analyses, such as studies here, that involve non-linearities and other deviations from ideal behavior, we do not fully understand these differences in performance."
The ability of any signal processing technique to extract information from spectroscopic data for determination of glucose concentrations relies heavily on the processes capability to account for nonlinearities, such a nonlinearities which can result from light penetrating skin at depths greater than 0.5 mm. Other sources of nonlinear relationships between spectral response and analyte concentrations can occur as a result of the instrumentation used, inter-constituent interactions, detector nonlinearities, etc. In the case of the above-identified '581 Robinson et al. patent, the use of chemometric calibration models (partial least squares and principal component regression) these nonlinearities cannot be totally accounted for by using linear models to fit nonlinear data. They will provide adequate results only if the nonlinear data is "linear" over a small region. However, this is not guaranteed to be the case, and therefore erroneous data can occur which will result in improper prediction of glucose concentrations. In an article recently published by P. J. Gemperline, et al entitled "Nonlinear Multivariate Calibration Using Principal Components Regression and Artificial Neural Networks," Analytical Chemistry, Vol. 63, No. 20, 1991 pp. 2313-2323 and an article published by P. Bhandare et al entitled "Multivariate Determination of Glucose in Whole Blood Using Partial Least-Squares and Artificial Neural Networks Based on Mid-Infrared Spectroscopy", Applied Spectroscopy, Vol, 47, No. 8, 1993, pp. 1214-1221, principal component regression (PCR) with an artificial neural network and partial least squares (PLS) with an artificial neural network, respectively, were used for detecting and modeling nonlinear regions of spectral response in multivariate, multi-component spectroscopic assays and determination of glucose concentrations, respectively. Although, these researchers claim improved results over the standard chemometric methods, they are continuing to use linear models for the nonlinear data. Their use of PCR and PLS techniques is for the purpose of signal characterization, i.e. feature extraction or data compression, such that, the reduced data sets which results are used as exemplars which are in turn used as inputs to the artificial neural networks. However, the same problems can occur with these approaches as can occur with the PCR and PLS techniques alone, that is, they are using linear models to fit nonlinear data. The resultant extracted features based on these linear modeling techniques are then presented to the non-linear artificial neural network.
Another major problem associated with the use of partial least squares (PLS) and principal component regression (PCR) spectral modeling techniques, as used by Robinson et al., is the determination of the number of factors that must be retained in order to yield the best results. Too many, or too few, factors can lead to improper calibration in the modeling process. Also, there is no evidence of compensation for temperature variations that will occur in vivo and in vitro. Absorption spectroscopy can be very sensitive to variations in temperature.
The Rosenthal et al. U.S. Pat. No. 5,086,229, describes a number of scenarios which use one or more infrared emitting diodes (IRED) as a light source(s), with one or more photodetectors. Multivariate analysis is also employed. However, the spectral regions used to collect absorbance data appear to be selected such that absorbance contributions due to water are minimized without regard to maximizing spectral intensities with respect to glucose, using for example a comparative model calibrated by use of spectroscopic absorbance data at various wavelengths associated with glucose spectral characteristics and other inferring analytes. The spectra shown in FIG. 15 of their patent are referred to as the effective spectra of glucose in the human body. The effective spectra were determined by subtracting two spectra obtained by transmitting optical energy having a wavelength between 600 nm to 1100 nm, in 1 nm intervals, `through` the distal portion of an index finger. The resultant effective spectra are those associated with glucose and other reference substances. Therefore, the information which is sought, that is, the spectral intensities associated with glucose that can be related to various concentrations, are `buried` in the overall spectral distribution.
Since glucose metabolism involves a complex interplay of hormones, spectral intensities of certain other substances, which can have wavelength characteristics in this spectral region, will change in response to glucose metabolism. Therefore, intensity variations in this spectral region at certain wavelengths of interest, which are observed as glucose is metabolized, can change as a result of the presence of many other substances, such as those in protein and lipid metabolism, and not glucose alone. This complex system and its molecular interactions may also account in part for their inconclusive clinical tests. Also, in the Rosenthal et al scheme, normalized derivative analysis of data obtained at multiple wavelengths, surrounding infrared spectral peaks and troughs which are produced by the presence of glucose and other reference substances, is employed to relate changes in concentrations of glucose to changes in absorbance.
Although the derivative technique can offer the advantage of ascertaining more precisely the center of each absorbance peak, the use of derivative techniques, without prior sophisticated signal processing of the data to reduce noise, can lead to an enhancement of the noise as well as the signal of interest. This might have contributed to inconclusive clinical tests that were performed in 1991. Therefore, a well-defined decision-based approach is not readily apparent in their scheme/approach for determining glucose concentrations. Rosenthal et al. state that an instrument can be constructed which provides accurate blood glucose measurements, which would have to correct for inaccuracies resulting for each person's "wavelength uniqueness." This is an acknowledgment of the aforementioned problem, but there does not appear a viable approach to overcome it.
Correcting inaccuracies for each individual in this manner does not establish a stable baseline which would be constant over time. With a `user-customized` instrument approach, inaccurate calibrations could lead to erroneous glucose concentration readings. This unreliable information would in turn be used by diabetic patients for insulin adjustments. As with the approach described by Robinson et al, referenced above, Rosenthal et al also overlook fundamental principles of the optical properties of skin.
In summary, all of the above-described prior art techniques are unique in terms of the methods used to determine glucose concentrations. However, they all have a common problem associated with them. Those methods which extract spectroscopic data to determine concentrations of glucose, which are buried in an effective infrared spectrum, are not reliable, and thus do not produce repeatable results. The spectral information of glucose that must be extracted from the effective spectrum not only has other spectra which can overlap the glucose spectrum, but the spectral characteristics associated with glucose alone can be altered by molecular interactions with other analytes (inter-constituent interactions). This includes the effects of hydrogen bonding and matrix effects caused by materials used in the instrument that come into contact with body fluids or tissue. (See, for example, an edited conference paper by W. Miller et al., entitled "Matrix Effects and Accuracy Assessment in Clinical Chemistry", Archives of Pathology & Laboratory Medicine, Vol. 117, No. 4, pgs. 343-436, 1993.) These techniques also do not fully address the non-linear effects than can occur in this type of process.
Also, the near-IR spectral region is essentially featureless and has the disadvantage of low absorbance by organic substances compared to absorbencies in the mid-IR region. However, using an irradiating source in the mid-IR spectral region and detecting resulting absorbencies in the same spectral region has the problem of requiring a special detector which must be cooled with liquid nitrogen in order to obtain necessary sensitivity. In addition, penetration depths of mid-IR energy are limited compared to those obtained when a near-IR source is used. Also, using a mid-IR source and measuring spectral characteristics in the near-IR spectrum, which are overtones and combination bands of spectral features in the mid-IR region, are not necessarily advantageous, because the overtones and combination bands have an extremely low intensity compared to the fundamental frequency intensities in the mid-IR for glucose.
Other prior art techniques to determine concentrations of biological substances in body fluids use chemical, enzymatic, and/or immunological methods. These methods require invasive means either to draw blood for analysis or to implant the device subcutaneously. In the case of subcutaneously implanted chemical glucose sensors, several major drawbacks are evident. First, the life-times of the devices are very limited, so that most of the current devices must be replaced within a few days. Secondly, the enzymatically impregnated membrane experience cell growth over them and deposition on extra-cellular secretions which drastically diminish the effectiveness of the device.